mlsanityData to 14 Oct 2026

CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization

arXiv 2105.14201 ↗First posted 29 May 2021cs.AI cs.IR cs.LGcited by 1

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Abstract (arXiv)

Timeline summarization (TLS) involves creating summaries of long-running events using dated summaries from numerous news articles. However, limited data availability has significantly slowed down the development of timeline summarization. In this paper, we introduce the CNTLS dataset, a versatile resource for Chinese timeline summarization. CNTLS encompasses 77 real-life topics, each with 2524 documents and summarizes nearly 60% days duration compression on average all topics. We meticulously analyze the corpus using well-known metrics, focusing on the style of the summaries and the complexity of the summarization task. Specifically, we evaluate the performance of various extractive and generative summarization systems on the CNTLS corpus to provide benchmarks and support further research. To the best of our knowledge, CNTLS is the first Chinese timeline summarization dataset. The dataset and source code are releasedfootnoteCode and data available at: emphurlhttps://github.com/OpenSUM/CNTLS..

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 29 May 2021.

Builds on

9 of 40 references.

References · 9

  1. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  2. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 294
  3. 2018Improving Abstraction in Text Summarizationcited by 35
  4. 2021Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networkscited by 32
  5. 2020Examining the State-of-the-Art in News Timeline Summarizationcited by 27
  6. 2020SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compressioncited by 24
  7. 2018A Temporally Sensitive Submodularity Framework for Timeline Summarizationcited by 17
  8. 2017Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarizationcited by 7
  9. 2017Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Eventscited by 2

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Citing papers per year: 2025: 1.

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  1. 2025TIM: A Large-Scale Dataset and large Timeline Intelligence Model for Open-domain Timeline Summarizationcited by 0